# specify sample size
N = 10000
T = 260

# specify quantiles to be used and approximation degrees K
quant_vec = [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.80, 0.85, 0.9, 0.95, 0.975]

quant_sel = zeros(Int8, 10,22)

quant_sel[1,7]  = 1 # 0.25
quant_sel[1,12] = 1 # 0.5
quant_sel[1,17] = 1 # 0.75

quant_sel[2,:] = quant_sel[1,:];
quant_sel[2,4] = 1; # 0.10
quant_sel[2,20] = 1; # 0.90

quant_sel[3,:] = quant_sel[2,:];
quant_sel[3,3] = 1; # 0.5
quant_sel[3,21] = 1; # 0.95

quant_sel[4,:] = quant_sel[3,:];
quant_sel[4,1] = 1; # 0.01
quant_sel[4,2] = 1; # 0.025

quant_sel[5,:] = quant_sel[4,:];
quant_sel[5,5] = 1;
quant_sel[5,9] = 1;
quant_sel[5,15] = 1;
quant_sel[5,19] = 1;

quant_sel[6,:] = quant_sel[5,:];
quant_sel[6,6] = 1;
quant_sel[6,10] = 1;
quant_sel[6,14] = 1;
quant_sel[6,18] = 1;

# quant_sel[7,:] = quant_sel[6,:];
# quant_sel[7,8] = 1;
# quant_sel[7,11] = 1;
# quant_sel[7,13] = 1;
# quant_sel[7,16] = 1;
#

quant_sel = quant_sel[1:5,:]


K_vec     = sum(quant_sel,dims=2)
K_vec_n   = length(K_vec)
K_vec     = K_vec + ones(Int8, K_vec_n)

# specify grid that is used to evaluate p(x)
xmin = 0
# xmax = 8
xmax = 2.6 #adjusted for standardization by capital stock
xn = 100
xgrid = range(xmin, stop=xmax, length=xn);


const TopCodeFlag = 0

# Option 1: linear - cubic - cubic, constructed from the left
##############################################################
function basis_logspline(x,knots)
    # this procedure evaluates the spline basis functions
    # at the n*1 vector x, given the (K-1)*1 vector of knots
    # output is  n*K

    x_dim  = length(x)
    if x_dim == 1
        x=[x]
    end

    basis_fcn = copy(x)

    # cubic part
    for i=1:length(knots)
        basis_fcn = [basis_fcn  max.((x .- ones(x_dim)*knots[i]),zeros(x_dim)).^3]
    end
    return basis_fcn
end
